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[Objective] The extraction and change monitoring of cultivated land information is one of the hotspots of remote sensing application research. Northern Guangdong is the main grain base of Guangdong Province, and is the key area of cultivated land change monitoring by business management departments.[Method] In the present paper, using GF-2 and Sentinel-2 remote sensing images, combined with the phenological period of late rice in the study area, the vegetation index, humidity index, brightness index, color index and texture features were extracted for multi-scale segmentation, and the multi-scale image of paddy field extraction was constructed. The index difference image and time series multi feature difference image were classified to extract the paddy field area and evaluate the results.[Result]（i) The highest extraction accuracy of paddy field in time series multi feature difference image was 0.98,and the minimum difference between paddy field area and late rice sowing area was 240.05 hm2,and the best paddy field extraction effect was based on time series multi feature difference image;（ii）The difference of index feature needed to use the key phenological characteristics of crops and combine with multi-scale segmentation of image to improve the accuracy of paddy field extraction;（iii）The machine forest method has faster operation speed and higher classification accuracy in data classification with high dimensional features. [Conclusion] The research of the paper is to explore the method of high-resolution remote sensing in rapid and accurate updating of cultivated land information, which can provide support for the application of cultivated land management business. [ABSTRACT FROM AUTHOR]

【Objective】Land use is comprehensively affected by natural conditions and socio-economic factors, and the study of time series changes in land use is conducive to providing a scientific basis for the rational use of urban land resources.【Method】Landsat satellite remote sensing images of six periods were selected at five-year intervals for land use classification from 1995 to 2020, and the land use structure, dynamics and land use transfer in Guangzhou were analyzed. The influence intensity and interaction mechanism of various factors on land use change in different periods were discussed by using geographic detectors. 【Result】（i） Under the scenario of long time series（1995-2020） urban land use change, geodetector model could analyze the factors of urban land use change in different periods and reveal its change rules.（ii） From 1995 to 2020, the construction land in Guangzhou showed a trend of ‘stable-slow increase-sharp increase’, while the cultivated land showed a trend of ‘slow decrease-stable-substantial decrease’, and the transferred area mainly came from cultivated land and forest land. The forest land and grassland showed a monotonous decreasing trend, mainly transferred out as construction land and forest land. The water area fluctuated up and down, mainly from cultivated land. Unused land was gradually reduced, mainly converted into construction land.（iii）In different periods from 1995 to 2020, the influence of driving factors of land use change in Guangzhou was different. Among them, the influence of socioeconomic factors on land use change was higher than that of physical geography.（iv） In different periods from 1995 to 2020, the q value of the interaction factor was higher than that of the single factor q value, which showed that the interaction between the factors was two-factor enhancement. 【Conclusion】The impacts of human activities on land use change are obviously different in spatial distribution. The overlapping of multiple factors will aggravate the conflict of land use. Reducing the impact of human activities on the environment is an important way to achieve sustainable development. [ABSTRACT FROM AUTHOR]